http://www.youtube.com/watch?v=3EeJCln5KYg

+

Robotics

CS311, Spring 2013David Kauchak

Some material adapted from slides from Zach Dodds

+Admin

Assignment 5 graded

Exam #2 available later today To be done by Sunday at midnight

+What is a robot?

"I can't define a robot, but I know one when I see one.”--Joseph Engelberger (1966)

Justice Potter Stewart wrote in Jacobellis v. Ohio (1964), "I can't define pornography, but I know it when I see it."

Robot Defined

Word robot was coined by a Czech novelist Karel Capek in a 1920 play titled Rossum’s Universal Robots (RUR)

Robota in Czech is a word for worker or servant

Definition of robot:Any machine made by one our members: Robot Institute of America

A robot is a reprogrammable, multifunctional manipulator designed to move material, parts, tools or specialized devices through variable programmed motions for the performance of a variety of tasks: Robot Institute of America, 1979

Karel Capek

What is a Robot

Manipulator

What is a Robot

Wheeled RobotLegged Robot

What is a Robot

Unmanned Aerial VehicleAutonomous Underwater Vehicle

Robot Plot

Bar Monkey (9)

Roomba (7) Genghis (3)

Stanford Cart (3)Shakey (3)MERs (8)

Sims (5)

Capability (0-10)

Stanley/Boss (9)

Autonomyhuman-controlled independent

less

World Modeling

more

Unimate (4)da Vinci (2)

Robot timeline?

...1921 2421215020201950

Fictional Robot timeline

...1921 242121502020

Put these robots in chronological order?

Fictional robot timeline

...1921 2421215020201950

I, Robot

Karl CapekRossum’s Universal Robots

Asimov

Real robot timeline

...1951 1968 1976 1985

Real robot timeline

...1951

Tortoise “Elsie”

by Neurophysiologist Grey Walter

http://www.frc.ri.cmu.edu/~hpm/talks/revo.slides/1950.html

Shakey

Nils Nilsson @ Stanford Research Inst.

......1968

first “general-purpose” mobile platform

Living Room (L)

rem

sp

Kitchen (K)

Bedroom (B)

sh tv

Go(from,to)Preconditions: At(sh,from)

Postconditions: At(sh,to)

Push(obj,fr,to) Preconditions: At(sh,fr) At(obj,fr)

Postconditions: At(sh,to) At(obj,to)

Robotics's Shakey start

START

GOAL

ACTIONS

Go(L,B)

Go(L,K)

At(sh,L) At(sp,K) At(rem,B) At(tv,L)

Push(tv,L,B)

Push(tv,L,K)

At(sh,K) At(sp,K) At(rem,B) At(tv,K)

At(sh,L) At(sp,L) At(rem,L) At(tv,L)

+Shakey in video

http://www.youtube.com/watch?v=qXdn6ynwpiI

Stanford Cart: SPA

Hans Moravec @ SAIL

......1976

SE

NS

ING

AC

TIN

G

perc

eptio

n

wor

ld m

odel

ing

Pla

nnin

g

task

exe

cutio

n

mot

or c

ontr

ol

“functional” task decomposition“horizontal” subtasks

Cartland (outdoors)

Cartland (indoors)

“Robot Insects”

Rodney Brooks @ MIT

......1985

avoid objects

wander

explore

build maps

identify objects

planning and reasoning

SEN

SIN

G

AC

TIN

G

“behavioral” task decomposition“vertical” subtasks

+Robotics

What are the challenges?How do these relate to AI?

+AI

Search planning

Game playing

CSPs

Bayesian

HMMs

Machine learning neural nets

Knowledge representation

Natural Language processing

Computer vision

how much of the world do we need to represent internally ?

how should we internalize the world ?

what outputs can we effect ?

what inputs do we have ?

what algorithms connect the two ?

how do we use this “internal world” effectively ?

Autonomy/behavior

Robot Architecture

Robot Architecture

how much / how do we represent the world internally ?

Task-specific

Not at all

As much as possible!

Reactive paradigm

SPA paradigm

Behavior-based architecture

As much as possible.

Hybrid approaches

sense

plan act

history…

Sense - Plan - Act

......1976

SEN

SIN

G

AC

TIN

G

perc

epti

on

worl

d

modelin

g

pla

nnin

g

task

exe

cuti

on

moto

r co

ntr

ol

sense

plan act

Stanford Cart

Shakey

1968

MERs

… - 2009

Mars Exploration Rovers

Sense – Plan – Act "deliberative" architecture

Mars Science Lab

2011 - lasers, lifebio, and maybe nuclear-powered

Robot Architecture

how much / how do we represent the world internally ?

Task-specific

Not at all

As much as possible!

Reactive paradigm

SPA paradigm

Behavior-based architecture

As much as possible.

Hybrid approaches

sense

plan act

sense

act

stimulus - response

Biological Inspiration

Ethology: describing animal behavior

Getting to the ocean?

AI reasoning systems abstract too much away: frame problem

sense

act

Decision-making is based only on current sensor inputs.

“The world is its own best model”

Digger wasps’ nest-building sequence

Analog reactive robots

...1951

“Tortoise” Gray Walter

1984

“BEAM”Mark Tilden

“light-headed” behavior

stateless...

http://people.cs.uchicago.edu/~wiseman/vehicles/

1989-

Valentino Braitenberg

robot made from Playstation pieces…!

http://haroldsbeambugs.solarbotics.net/mercury.htm

commercial products…

Robot Architecture

how much / how do we represent the world internally ?

Task-specific

Not at all

As much as possible!

Reactive paradigm

SPA paradigm

Subsumption paradigmPotential Fields

Behavior-based architecture

As much as possible.

Hybrid approaches

sense

plan act

sense

act

different ways of composing behaviors

stimulus – response == "behavior"

......1985

avoid objects

wander

explore

build maps

identify objects

planning and reasoning

SEN

SIN

G

AC

TIN

G

Genghis

“Vertical” task decomposition

sense

act

little explicit deliberation except through system

state

Behavior-based control

Behavior a direct mapping of sensory inputs to a pattern of task-specific motor actions

extinguish approach wander

Subsumption

Subsumption builds intelligence incrementally in layers

runaway behavior

wander behavior

Subsumption

Where would a light-seeking behavior/layer connect?

runaway behavior

wander behavior

Subsumption

Where would a light-seeking behavior/layer connect?

runaway behavior

wander behavior

S

Closest LightLIGHT

SONAR

phototaxis

Subsumption - Limits

Success of behavior-based systems depends on how well-tuned they are to their environment. This is a huge strength, but it's also a weakness …

Herbert, a soda-can-collecting robot

Reaching the end of the subsumption architecture and

purely reactive approaches.

http://www.youtube.com/watch?v=YtNKuwiVYm0

Subsumption limits: Genghis

runaway behavior

wander behavior

navigate behavior

FSM / DFA

Unwieldy!

Larger example -- Genghis

1) Standing by tuning the parameters of two behaviors: the leg “swing” and the leg “lift”

2) Simple walking: one leg at a time

3) Force Balancing: via incorporated force sensors on the legs

4) Obstacle traversal: the legs should lift much higher if need be

5) Anticipation: uses touch sensors (whiskers) to detect obstacles

6) Pitch stabilization: uses an inclinometer to stabilize fore/aft pitch

7) Prowling: uses infrared sensors to start walking when a human approaches

8) Steering: uses the difference in two IR/range sensors to follow

57 modules wired together !

Robot Architecture

how much / how do we represent the world internally ?

Task-specific

Not at all

As much as possible!

Reactive paradigm

SPA paradigm

Subsumption paradigmPotential Fields

Behavior-based architecture

As much as possible.

Hybrid approaches

sense

plan act

sense

act

different ways of composing behaviors

Potential Fields

Potential fields compose simple behaviors by adding the outputs that each sensor/input sends the robot

Ron Arkin @ Georgia Tech

A sequencing process (FSM/DFA) updates the potential fields and/or decides which ones to run next…

Individual potential fields (motor schemas) contain state

Motor Schemas / Potential Fields

goal-seeking schemaobstacle-avoiding schema

note that the complete environmental vector fields are only for visualization!

Direct mapping from the environment to a control signal

combine?

Behavior Summer

vector sum of the avoid and goal motor schemas

path taken by a robot controlled by the resulting

field

Implementation details

the extent to which potential field force drops off with distance…

corridor-following schema(s)?what crucial assumption is being made here?

Additional behavior primitives

go! schemacorridor-centering schema

A more complex task

Direct mapping from the environment to a control signal

larger composite task

How many individual fields are summed in this task?

Not necessarily all at one time!

Local minima

A potential-field-based system can get stuck!

the problem

a solution?

What would happen if a robot came in in the middle on the left?

Local minima

A potential-field-based system can get stuck!

the problem

Why is the “local minimum” problem, as illustrated to the left, not likely to actually cause a robot to get stuck in practice?

robots controlled by summing goal/obstacle potential fields can get stuck in practice -- draw an example of an environment with both obstacle(s) and goals(s) in which getting stuck might actually occur.

Suggest how a robot might overcome the problem of getting stuck in such cases…

Local minima

A potential-field-based system can get stuck!

the problem a solution

Bigger deadends...

How to get out of larger wells ?

Bigger deadends...

uses memory of where the robot has been

past-avoiding motor schema

Another example

Keeping away from past locations...

Pfields in Practice

Steathy navigation @ USC (Ashley Tews, Gaurav S. Sukhatme, and Maja J. Mataric)

part of the potential field… What's going on here?

http://robotics.usc.edu/interaction/?l=Research:Projects:stealth:index#experiments

Docking with potential fields

How does the idea of docking, e.g., with an electrical outlet change the requirements for a potential field?

example goals

Why might a simple attractive force not be sufficient for docking (plugging-in, etc.)?

Docking with potential fields

The key insight is the need to establish an approach direction

example goals

Docking with potential fields

The key insight is the need to establish an approach direction

+Review

Machine learning general learning concepts

supervised vs. unsupervised features/feature-based problems/feature space bias/variance overfitting hyperplanes/linear seperability

Supervised learning applications approaches

k-NN decision trees NB SVM (large margin classifiers)

Ensemble approaches (boosting)

+Review

Machine learning (continued) unsupervised learning

application issues

number of clusters flat vs. hierarchical soft vs. hard clustering

approaches k-means EM

word alignment clustering (mixture of gaussians)

spectral clustering (min-cut)

+Review

Neural networks (Machine learning?) perceptrons/neurons

activation functions (threshold vs. sigmoid) perceptron learning

multi-layer networks

Knowledge representation basic logic ontology NELL

+Review

CSPs problem formulation

variables domain constraints

why CSPs? applications? constraint graph CSP as search

backtracking algorithm forward checking arc consistency

heuristics most constrained variable least constrained value ...

+Review

Natural language processing Applications Problem areas Why it’s hard? Machine translation setup

+Guest speaker

Rodney Brooks Professor at MIT (was previous director of CSAIL) Founder of iRobot

http://www.youtube.com/watch?v=B79D9nW2AFA